Verleysen M, "The curse of dimensionality in data mining and time series prediction", In Computational Intelligence and Bioinspired system, IWANN(2005), pp. 758-70Verleysen M,D Francois.The curse of dimensionality in data mining and time series prediction[C].In:Vilanova i la Geltru,Spain:...
Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as $$k$$ -neares
But mining in high dimensional data is extraordinarily difficult because of the curse ofdimensionality. 由于这种数据存在的普遍性,使得对高维数据挖掘的研究有着非常重要的意义. 互联网 These regenerative and beneficial relationships give a system complexitydimensionality, and thus, resiliency. ...
Data accumulates in an unprecedented speed Data preprocessing is an important part for effective machine learning and data mining Dimensionality reduction is an effective approach to downsizing data 4 Most machine learning and data mining techniques may not be effective for high- ...
A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction i...
However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of ...
Data mining has become a rapidly growing field in recent years. At the same time, data generation has seen a surge in volume, leading to a growth in size, complexity, and data dimensionality. High-dimensional data exists where the number of data features is on the order of the number of...
The curse of dimensionality (COD) was first described by Richard Bellman, a mathematician, in the context of approximation theory. In data analysis, the term refers to the difficulty of finding hidden structure when the number of variables is large. For all problems in data mining, one can sh...
For many large-scale applications in data mining, machine learning, and multimedia, fundamental operations such as similarity search, retrieval, classification, clustering, and anomaly detection generally suffer from an effect known as the `curse of dimensionality'. As the dimensionality of the data in...
interpolets, prewavelets, or wavelets can be used in a straightforward way.We describe the basic features of sparse grids and report the results of various numerical experiments for the solution of elliptic PDEs as well as for other selected problems such as numerical quadrature and data mining...